Abstract
This paper proposes an auxiliary model based hierarchical least squares algorithm for multivariable Box-Jenkins-like systems using the hierarchical identification principle. To improve the computational efficiency, a multivariable system is decomposed into two subsystems by using the data filtering technique. Furthermore, this paper presents a data filtering based auxiliary model hierarchical least squares algorithm for multivariable Box-Jenkins-like systems. The simulation example shows that the proposed identification algorithms are effective.
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